1. Field of the Invention
This invention relates generally to the field of investment risk management and, more specifically, to a system, method, and computer program product for generating risk forecasts by predicting future volatility of single stocks and portfolios of stocks.
2. Discussion of the Background
Accurate and meaningful risk analysis is essential to superior investment performance. A standard definition of risk is the dispersion or volatility of returns for a single asset or a portfolio of assets, usually measured by standard deviation.
Standard portfolio theory, and modern analogues embodied in a range of value-at-risk (VaR) models, require estimates of volatility and covariance between stock returns in order to generate a risk forecast. It is well known that using the naive sample covariance matrix leads to unreliable risk forecasts simply because too many parameters have to be estimated from too little data. As an example, for a portfolio of 200 stocks, 20,100 parameters should be estimated in order to obtain the necessary covariance matrix. This is a manifestation of the so-called curse of dimensionality. Further, the out-of-sample forecasting performance of this naive estimate is hampered due to giving too much weight to the idiosyncratic component of risk.
The most common way to deal with this problem is to impose some structure on stock returns. In other words, it is assumed that stock returns are driven by several common factors. Consequently, volatility of stock returns can largely be explained by the volatility of factor returns. The so-constructed risk model is called a factor risk model, which provides a simple framework to reduce the curse of dimensionality and to identify sources of risk. In addition, a factor risk model makes it tractable to filter outliers and obtain more robust risk estimates. Additionally, a factor risk model makes it workable to achieve more accurate, forward-looking, risk forecasts.
A proper factor risk model has to address the following issues. First, it must be feasible to estimate. Second, it has to be intuitive to use. Third, it has to be parsimonious enough to avoid over-fitting and guarantee adequate out-of-sample performance. Finally, it must reflect commonalities in stock returns in order to reduce noise and to achieve the decompositions desired in making investment decisions such as hedging, bench marking, performance attribution, and segmented analysis.